Unlocking Cancer's Ancestry Code

How Genetic Roots Shape Tumors and Transform Treatment

The Invisible Inequality in Cancer Care

Cancer doesn't strike equally. For decades, stark racial disparities have persisted in cancer incidence, treatment access, and survival rates. Black Americans die from breast cancer at 40% higher rates than White patients, while Hispanic populations face 22% higher incidence of advanced thyroid cancer 4 .

Beyond socioeconomic barriers lies a hidden factor: tumor biology itself may behave differently across ancestral lines. Until recently, this genetic dimension remained obscured by a critical data gap—medical records frequently omit race/ethnicity, and genomic databases overwhelmingly represent European ancestry .

Enter a landmark study of 100,000 cancer patients that pioneered a revolutionary solution: using tumor DNA to infer continental ancestry. This approach uncovered startling inequities in who receives cutting-edge genomic profiling—and how ancestry shapes cancer's behavior 4 5 .

Disparity Snapshot

Racial disparities in cancer outcomes based on recent studies 4 .

Decoding Ancestry Through Tumor DNA: The 100,000-Patient Experiment

The Ancestry Inference Toolkit

When medical records lack racial/ethnic data, how can researchers study disparities? The Tempus xT study team deployed ancestry-informative markers (AIMs)—654 DNA variants with distinct frequencies across continental populations. Like genetic GPS coordinates, these markers pinpoint ancestral origins from tumor sequencing data 4 .

Methodology Breakdown
  1. Sequencing: Tumor/normal DNA from 100k patients sequenced via Tempus xT panel (648 cancer genes)
  2. Ancestry Inference:
    • AIMs overlapping capture regions identified
    • Supervised algorithms assigned proportions of African, European, East Asian, Amerindian, South Asian ancestry
  3. Race/Ethnicity Imputation:
    • Thresholds applied (e.g., >50% African ancestry = Non-Hispanic Black)
    • Validation against self-reported labels: <2% error rate 4
Cancer Disparities Unmasked by Genetic Ancestry
Cancer Type Over/Under-Represented Group Disparity Magnitude
Pancreatic Black patients -18% below expected
Gallbladder Asian patients +32% above expected
Colorectal Hispanic patients +22% above expected
Urinary Tract Black patients -42% below expected

Data source: Tempus real-world cohort (N=100,000) 4 5

The Shocking Disparities Revealed

The imputed ancestry data exposed systemic testing gaps:

  • Pancreatic cancer: Black patients underrepresented by 18% despite higher incidence 1
  • Advanced profiling access: Non-Hispanic Whites comprised 72% of tested patients vs. 11% for Blacks 5 2
  • Hidden diversity: Ancestry analysis identified 85% more Black patients and 150% more Hispanic patients than medical records alone 4 3
Genomic Database Diversity Gap

Tempus still shows European overrepresentation but vastly improves diversity 4

Why Ancestry Matters in Tumor Biology

Genetic roots influence cancer in two profound ways:

Mutation Frequency Variations
  • Breast cancer: Black metastatic patients have fewer PIK3CA mutations (targetable by alpelisib) than White/Asian patients
  • Survival-linked genes: APC, EGFR, STK11, TP53 mutations show ancestry-dependent prevalence and survival impacts 8
Gene Expression Patterns
  • Colombian studies reveal Native American ancestry protects against breast cancer, while European ancestry increases risk 6
  • Ancestral background shapes tumor microenvironments and drug responses 6 8

The Human Impact: When Mistrust Meets Molecular Medicine

Black patients' genomic disparities stem partly from healthcare access gaps—but medical mistrust plays a crucial role:

33%

of Black cancer patients had never heard of tumor genomic profiling (TGP)

49%

were unaware if they'd received TGP 2

37%

lower genetic counseling rates than Whites 7

Patient Perspectives

"Doctors don't explain things in ways we understand. They rush." 2

  • Historical trauma: Tuskegee and Henrietta Lacks cases drive ongoing suspicion
  • Communication breakdown: Cultural barriers in medical explanations

The Scientist's Toolkit: Ancestry Inference Technologies

Technology Function Example
Ancestry-informative markers (AIMs) SNPs with large allele frequency differences between populations Tempus xT's 654 AIMs 4
Supervised ancestry algorithms Matches individual genotypes to reference population data ADMIXTURE (used in Colombian study) 6
Synthetic data platforms Generates hybrid genomes to train ancestry models RAIDS package's data synthesis 1
Transcriptome-based inference Estimates ancestry from RNA-seq data CSHL's tumor RNA methods 3

Toward Equity: Solutions in Action

Closing the ancestry knowledge gap requires multi-pronged strategies:

Diversify Genomic Databases
  • CSHL's Krasnitz team now refines ancestry calls to regional levels (e.g., West vs. East Africa) 3
  • Colombian projects validate that germline panels (e.g., TruSightâ„¢) can accurately capture ancestry with 96% correlation to WGS 6
Reengineer Patient Engagement
  • Pre-test education videos featuring diverse narrators improve understanding 7
  • Centralized support systems outside oncology clinics build trust 2
Policy Shifts
  • FDA diversity mandates for clinical trials (e.g., 2024 FDORA Act)
  • Insurance coverage for ancestry-informed tumor testing in Medicaid plans

Conclusion: Precision Medicine's Equity Imperative

"Why do people of different races get sick at different rates? They have different habits, exposures—all kinds of factors. But there may be a genetic component too." 3

Alexander Krasnitz, Cold Spring Harbor Laboratory

The 100,000-patient study proves that ignoring ancestry blinds oncology to biological realities. By harnessing tumor DNA as an ancestry compass, we can finally steer cancer care toward true equity—where precision medicine serves all populations, not just the best-represented.

Key Terms

Tumor genomic profiling (TGP)

Sequencing tumor DNA to identify targetable mutations

Ancestry-informative markers (AIMs)

Genetic variants with major frequency differences across populations

Germline testing

Analyzes inherited DNA variants (vs. somatic tumor mutations)

References